Overview

Dataset statistics

Number of variables13
Number of observations2969
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.7 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with qtde_invoices and 4 other fieldsHigh correlation
recency_days is highly correlated with qtde_invoicesHigh correlation
qtde_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
qtde_items is highly correlated with gross_revenue and 4 other fieldsHigh correlation
qtde_products is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with gross_revenue and 2 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_unique_basket_size is highly correlated with qtde_productsHigh correlation
qtde_returns is highly correlated with avg_basket_sizeHigh correlation
avg_ticket is highly skewed (γ1 = 53.4442279) Skewed
frequency is highly skewed (γ1 = 24.88037069) Skewed
qtde_returns is highly skewed (γ1 = -51.79774426) Skewed
avg_basket_size is highly skewed (γ1 = 44.68328098) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 34 (1.1%) zeros Zeros
qtde_returns has 1481 (49.9%) zeros Zeros

Reproduction

Analysis started2022-11-04 13:12:03.077980
Analysis finished2022-11-04 13:12:14.064621
Duration10.99 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2317.292354
Minimum0
Maximum5715
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-04T10:12:14.097318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.4
Q1929
median2120
Q33537
95-th percentile5035.2
Maximum5715
Range5715
Interquartile range (IQR)2608

Descriptive statistics

Standard deviation1554.944589
Coefficient of variation (CV)0.6710178739
Kurtosis-1.010787014
Mean2317.292354
Median Absolute Deviation (MAD)1271
Skewness0.342284058
Sum6880041
Variance2417852.674
MonotonicityStrictly increasing
2022-11-04T10:12:14.142106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
30111
 
< 0.1%
29961
 
< 0.1%
29991
 
< 0.1%
30001
 
< 0.1%
30011
 
< 0.1%
30021
 
< 0.1%
30051
 
< 0.1%
30071
 
< 0.1%
30081
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
57151
< 0.1%
56961
< 0.1%
56861
< 0.1%
56801
< 0.1%
56591
< 0.1%
56551
< 0.1%
56491
< 0.1%
56381
< 0.1%
56371
< 0.1%
56271
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2969
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.77299
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-04T10:12:14.188008image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.4
Q113799
median15221
Q316768
95-th percentile17964.6
Maximum18287
Range5940
Interquartile range (IQR)2969

Descriptive statistics

Standard deviation1718.990292
Coefficient of variation (CV)0.1125673398
Kurtosis-1.206094692
Mean15270.77299
Median Absolute Deviation (MAD)1488
Skewness0.03160785866
Sum45338925
Variance2954927.624
MonotonicityNot monotonic
2022-11-04T10:12:14.233788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
175881
 
< 0.1%
149051
 
< 0.1%
161031
 
< 0.1%
146261
 
< 0.1%
148681
 
< 0.1%
182461
 
< 0.1%
171151
 
< 0.1%
166111
 
< 0.1%
159121
 
< 0.1%
Other values (2959)2959
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2954
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.226056
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-04T10:12:14.280722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile229.77
Q1570.96
median1086.92
Q32308.06
95-th percentile7219.68
Maximum279138.02
Range279131.82
Interquartile range (IQR)1737.1

Descriptive statistics

Standard deviation10580.4905
Coefficient of variation (CV)3.848534202
Kurtosis353.9585684
Mean2749.226056
Median Absolute Deviation (MAD)672.72
Skewness16.77787915
Sum8162452.16
Variance111946779.3
MonotonicityNot monotonic
2022-11-04T10:12:14.324242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178.962
 
0.1%
533.332
 
0.1%
889.932
 
0.1%
2053.022
 
0.1%
745.062
 
0.1%
379.652
 
0.1%
2092.322
 
0.1%
731.92
 
0.1%
1353.742
 
0.1%
3312
 
0.1%
Other values (2944)2949
99.3%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
151
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
140438.721
< 0.1%
124564.531
< 0.1%
117375.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.28864938
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-04T10:12:14.369559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.75617089
Coefficient of variation (CV)1.209485215
Kurtosis2.778038567
Mean64.28864938
Median Absolute Deviation (MAD)26
Skewness1.798396863
Sum190873
Variance6046.022112
MonotonicityNot monotonic
2022-11-04T10:12:14.413094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
285
 
2.9%
385
 
2.9%
876
 
2.6%
1067
 
2.3%
966
 
2.2%
766
 
2.2%
1764
 
2.2%
2255
 
1.9%
Other values (262)2219
74.7%
ValueCountFrequency (%)
034
 
1.1%
199
3.3%
285
2.9%
385
2.9%
487
2.9%
543
1.4%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3724
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

qtde_invoices
Real number (ℝ≥0)

HIGH CORRELATION

Distinct56
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.72280229
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-04T10:12:14.460129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.85665393
Coefficient of variation (CV)1.547607882
Kurtosis190.8253633
Mean5.72280229
Median Absolute Deviation (MAD)2
Skewness10.76645634
Sum16991
Variance78.44031883
MonotonicityNot monotonic
2022-11-04T10:12:14.503793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2786
26.5%
3498
16.8%
4393
13.2%
5237
 
8.0%
1190
 
6.4%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
Other values (46)332
11.2%
ValueCountFrequency (%)
1190
 
6.4%
2786
26.5%
3498
16.8%
4393
13.2%
5237
 
8.0%
6173
 
5.8%
7138
 
4.6%
898
 
3.3%
969
 
2.3%
1055
 
1.9%
ValueCountFrequency (%)
2061
< 0.1%
1991
< 0.1%
1241
< 0.1%
971
< 0.1%
912
0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%
571
< 0.1%

qtde_items
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1665
Distinct (%)56.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1606.461098
Minimum1
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-04T10:12:14.549476image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101.4
Q1296
median639
Q31399
95-th percentile4407.4
Maximum196844
Range196843
Interquartile range (IQR)1103

Descriptive statistics

Standard deviation5882.976527
Coefficient of variation (CV)3.6620722
Kurtosis467.153716
Mean1606.461098
Median Absolute Deviation (MAD)420
Skewness17.87844459
Sum4769583
Variance34609412.81
MonotonicityNot monotonic
2022-11-04T10:12:14.594552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
889
 
0.3%
1509
 
0.3%
2608
 
0.3%
848
 
0.3%
2888
 
0.3%
2728
 
0.3%
2468
 
0.3%
5167
 
0.2%
3947
 
0.2%
Other values (1655)2886
97.2%
ValueCountFrequency (%)
11
< 0.1%
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
799631
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
628121
< 0.1%
582431
< 0.1%
577851
< 0.1%

qtde_products
Real number (ℝ≥0)

HIGH CORRELATION

Distinct469
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.705288
Minimum1
Maximum7837
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-04T10:12:14.643332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7837
Range7836
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.8419967
Coefficient of variation (CV)2.199106503
Kurtosis354.8373546
Mean122.705288
Median Absolute Deviation (MAD)44
Skewness15.70613971
Sum364312
Variance72814.70321
MonotonicityNot monotonic
2022-11-04T10:12:14.687797image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2845
 
1.5%
2038
 
1.3%
3535
 
1.2%
1533
 
1.1%
2933
 
1.1%
1933
 
1.1%
1132
 
1.1%
2631
 
1.0%
2730
 
1.0%
2529
 
1.0%
Other values (459)2630
88.6%
ValueCountFrequency (%)
16
 
0.2%
214
0.5%
316
0.5%
417
0.6%
526
0.9%
629
1.0%
718
0.6%
819
0.6%
927
0.9%
1027
0.9%
ValueCountFrequency (%)
78371
< 0.1%
56701
< 0.1%
50951
< 0.1%
45771
< 0.1%
26981
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16731
< 0.1%
16361
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct2966
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.90005685
Minimum2.150588235
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-04T10:12:14.733842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.150588235
5-th percentile4.916661099
Q113.11933333
median17.97438356
Q324.98828571
95-th percentile90.497
Maximum56157.5
Range56155.34941
Interquartile range (IQR)11.86895238

Descriptive statistics

Standard deviation1036.934336
Coefficient of variation (CV)19.9794451
Kurtosis2890.70744
Mean51.90005685
Median Absolute Deviation (MAD)5.994222271
Skewness53.4442279
Sum154091.2688
Variance1075232.818
MonotonicityNot monotonic
2022-11-04T10:12:14.775864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
152
 
0.1%
4.1622
 
0.1%
14.478333332
 
0.1%
18.152222221
 
< 0.1%
13.927368421
 
< 0.1%
36.244117651
 
< 0.1%
29.784166671
 
< 0.1%
22.87926231
 
< 0.1%
20.511041671
 
< 0.1%
149.0251
 
< 0.1%
Other values (2956)2956
99.6%
ValueCountFrequency (%)
2.1505882351
< 0.1%
2.43251
< 0.1%
2.4623711341
< 0.1%
2.5112413791
< 0.1%
2.5153333331
< 0.1%
2.651
< 0.1%
2.6569318181
< 0.1%
2.7075982531
< 0.1%
2.7606215721
< 0.1%
2.7704641911
< 0.1%
ValueCountFrequency (%)
56157.51
< 0.1%
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.98751
< 0.1%
872.131
< 0.1%
841.02144931
< 0.1%
651.16833331
< 0.1%
6401
< 0.1%
624.41
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.35143043
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-04T10:12:14.821006image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q125.92857143
median48.28571429
Q385.33333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.4047619

Descriptive statistics

Standard deviation63.54282948
Coefficient of variation (CV)0.9434518178
Kurtosis4.887703174
Mean67.35143043
Median Absolute Deviation (MAD)26.28571429
Skewness2.062908983
Sum199966.397
Variance4037.691178
MonotonicityNot monotonic
2022-11-04T10:12:15.026573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
422
 
0.7%
7021
 
0.7%
720
 
0.7%
3519
 
0.6%
4918
 
0.6%
2117
 
0.6%
4617
 
0.6%
1117
 
0.6%
116
 
0.5%
Other values (1248)2777
93.5%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
422
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1225
Distinct (%)41.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1137912226
Minimum0.005449591281
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-04T10:12:15.073307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.008894164194
Q10.01633986928
median0.02588996764
Q30.04941860465
95-th percentile1
Maximum17
Range16.99455041
Interquartile range (IQR)0.03307873537

Descriptive statistics

Standard deviation0.4081571514
Coefficient of variation (CV)3.586894861
Kurtosis989.3578171
Mean0.1137912226
Median Absolute Deviation (MAD)0.0121913375
Skewness24.88037069
Sum337.8461398
Variance0.1665922603
MonotonicityNot monotonic
2022-11-04T10:12:15.118997image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1198
 
6.7%
0.0277777777817
 
0.6%
0.062517
 
0.6%
0.0238095238116
 
0.5%
0.0909090909115
 
0.5%
0.0833333333315
 
0.5%
0.0344827586214
 
0.5%
0.0294117647114
 
0.5%
0.0357142857113
 
0.4%
0.0769230769213
 
0.4%
Other values (1215)2637
88.8%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054794520551
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
171
 
< 0.1%
31
 
< 0.1%
26
 
0.2%
1.1428571431
 
< 0.1%
1198
6.7%
0.751
 
< 0.1%
0.66666666673
 
0.1%
0.5508021391
 
< 0.1%
0.53351206431
 
< 0.1%
0.53
 
0.1%

qtde_returns
Real number (ℝ)

HIGH CORRELATION
SKEWED
ZEROS

Distinct214
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-62.1569552
Minimum-80995
Maximum0
Zeros1481
Zeros (%)49.9%
Negative1488
Negative (%)50.1%
Memory size23.3 KiB
2022-11-04T10:12:15.171354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-80995
5-th percentile-100.6
Q1-9
median-1
Q30
95-th percentile0
Maximum0
Range80995
Interquartile range (IQR)9

Descriptive statistics

Standard deviation1512.496135
Coefficient of variation (CV)-24.33349783
Kurtosis2765.52864
Mean-62.1569552
Median Absolute Deviation (MAD)1
Skewness-51.79774426
Sum-184544
Variance2287644.557
MonotonicityNot monotonic
2022-11-04T10:12:15.218009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01481
49.9%
-1164
 
5.5%
-2148
 
5.0%
-3105
 
3.5%
-489
 
3.0%
-678
 
2.6%
-561
 
2.1%
-1251
 
1.7%
-843
 
1.4%
-743
 
1.4%
Other values (204)706
23.8%
ValueCountFrequency (%)
-809951
< 0.1%
-90141
< 0.1%
-80041
< 0.1%
-44271
< 0.1%
-37681
< 0.1%
-33321
< 0.1%
-28781
< 0.1%
-20221
< 0.1%
-20121
< 0.1%
-17761
< 0.1%
ValueCountFrequency (%)
01481
49.9%
-1164
 
5.5%
-2148
 
5.0%
-3105
 
3.5%
-489
 
3.0%
-561
 
2.1%
-678
 
2.6%
-743
 
1.4%
-843
 
1.4%
-941
 
1.4%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct1973
Distinct (%)66.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean249.349541
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-04T10:12:15.267175image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44
Q1103.25
median172
Q3281.5
95-th percentile599.52
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.25

Descriptive statistics

Standard deviation791.5024106
Coefficient of variation (CV)3.174268569
Kurtosis2256.245507
Mean249.349541
Median Absolute Deviation (MAD)82.75
Skewness44.68328098
Sum740318.7873
Variance626476.066
MonotonicityNot monotonic
2022-11-04T10:12:15.312979image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
739
 
0.3%
869
 
0.3%
829
 
0.3%
1368
 
0.3%
608
 
0.3%
758
 
0.3%
888
 
0.3%
717
 
0.2%
Other values (1963)2882
97.1%
ValueCountFrequency (%)
12
0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
40498.51
< 0.1%
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%

avg_unique_basket_size
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1010
Distinct (%)34.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.15507374
Minimum1
Maximum299.7058824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2022-11-04T10:12:15.361578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.345454545
Q110
median17.2
Q327.75
95-th percentile56.94
Maximum299.7058824
Range298.7058824
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation19.51303316
Coefficient of variation (CV)0.8807478316
Kurtosis27.69469772
Mean22.15507374
Median Absolute Deviation (MAD)8.2
Skewness3.498252107
Sum65778.41393
Variance380.7584629
MonotonicityNot monotonic
2022-11-04T10:12:15.405492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1353
 
1.8%
1440
 
1.3%
1138
 
1.3%
2033
 
1.1%
933
 
1.1%
132
 
1.1%
1831
 
1.0%
1030
 
1.0%
1629
 
1.0%
1728
 
0.9%
Other values (1000)2622
88.3%
ValueCountFrequency (%)
132
1.1%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
0.1%
1.58
 
0.3%
1.5681818181
 
< 0.1%
1.5714285711
 
< 0.1%
1.6666666674
 
0.1%
1.8333333331
 
< 0.1%
224
0.8%
ValueCountFrequency (%)
299.70588241
< 0.1%
2591
< 0.1%
203.51
< 0.1%
1481
< 0.1%
1451
< 0.1%
136.1251
< 0.1%
135.51
< 0.1%
1271
< 0.1%
1221
< 0.1%
1181
< 0.1%

Interactions

2022-11-04T10:12:13.248287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:05.249445image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:05.947318image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.567915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:07.142232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.124852image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.684946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.271536image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.954631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.503243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.074822image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.657712image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:12.551047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.315590image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:05.371155image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:05.995361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.609013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:07.185477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.164343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.728798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.315387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.994728image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.545545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.120201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.706123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:12.601672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.357519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:05.444552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.043714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.653150image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:07.231706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.204206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.772482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.357677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.034936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.588049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.162501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.749340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:12.686892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.398725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:05.498240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.085416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.700003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:07.272952image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.243799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.815568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.400002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.075934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.636145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.204882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.793222image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:12.731928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.441637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:05.548212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.128379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.742982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:07.316693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.288719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.860520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.445850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.118549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.679380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.249216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.838300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:12.806104image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.481410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:05.589207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.167531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.790244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:07.356500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.326220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.902441image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.487266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.157075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.720368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.289276image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.879362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:12.853554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.525843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:05.636386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.211661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.834773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:07.819968image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.369618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.948723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.533212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.201516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.765395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.334066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.926657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:12.899454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.570146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:05.682342image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.289343image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.883652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:07.864513image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.412961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.999127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.579436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.245384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.809878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.379599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.973002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:12.946053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.611293image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:05.723610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.333321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.925929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:07.905922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.456583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.042071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.622063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.285950image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.852077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.421502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:12.144495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.016325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.666032image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:05.767749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.383182image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.969097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:07.948715image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.505711image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.086672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.770481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.329423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.895610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.465701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:12.190544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.064774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.716528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:05.811401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.428515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:07.011894image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:07.992478image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.554735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.132471image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.816315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.371411image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.939237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.510698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:12.236463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.110604image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.765663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:05.856969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.474878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:07.056662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.037309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.600080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.180109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.862643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.415390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.985459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.557575image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:12.311924image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.157862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.810148image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:05.904080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:06.523263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:07.101080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.081821image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:08.643841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.227861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:09.908719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:10.461362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.030722image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:11.614918image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:12.483665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-04T10:12:13.203672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-04T10:12:15.448744image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-04T10:12:15.524124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-04T10:12:15.602494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-04T10:12:15.678834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-04T10:12:13.907056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-04T10:12:14.027094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
00178505391.21372.034.01733.0297.018.15222235.50000017.000000-40.050.9705888.735294
11130473232.5956.09.01390.0171.018.90403527.2500000.028302-35.0154.44444419.000000
22125836705.382.015.05028.0232.028.90250023.1875000.040323-50.0335.20000015.466667
3313748948.2595.05.0439.028.033.86607192.6666670.0179210.087.8000005.600000
4415100876.00333.03.080.03.0292.0000008.6000000.073171-22.026.6666671.000000
55152914623.3025.014.02102.0102.045.32647123.2000000.040115-29.0150.1428577.285714
66146885630.877.021.03621.0327.017.21978618.3000000.057221-399.0172.42857115.571429
77178095411.9116.012.02057.061.088.71983635.7000000.033520-41.0171.4166675.083333
881531160767.900.091.038194.02379.025.5434644.1444440.243316-474.0419.71428626.142857
99160982005.6387.07.0613.067.029.93477647.6666670.0243900.087.5714299.571429

Last rows

df_indexcustomer_idgross_revenuerecency_daysqtde_invoicesqtde_itemsqtde_productsavg_ticketavg_recency_daysfrequencyqtde_returnsavg_basket_sizeavg_unique_basket_size
29595627177271060.2515.01.0645.066.016.0643946.01.000000-6.0645.00000066.0
2960563717232421.522.02.0203.036.011.70888912.00.1538460.0101.50000018.0
2961563817468137.0010.02.0116.05.027.4000004.00.4000000.058.0000002.5
2962564913596697.045.02.0406.0166.04.1990367.00.2500000.0203.00000083.0
29635655148931237.859.02.0799.073.016.9568492.00.6666670.0399.50000036.5
2964565912479473.2011.01.0382.030.015.7733334.01.000000-34.0382.00000030.0
2965568014126706.137.03.0508.015.047.0753333.00.750000-50.0169.3333335.0
29665686135211092.391.03.0733.0435.02.5112414.50.3000000.0244.333333145.0
2967569615060301.848.04.0262.0120.02.5153331.02.0000000.065.50000030.0
2968571512558269.967.01.0196.011.024.5418186.01.000000-196.0196.00000011.0